AI agents are increasingly the primary consumers of data, operating continuously to make concurrent, irreversible decisions. Traditional data systems designed for human analysis cycles become correctness bottlenecks under this operating regime. When multiple agents operate over shared resources, their actions interact before reconciliation is possible. Correctness guarantees that apply after the decision window therefore fail to prevent conflicts. We introduce the Decision Coherence Law: for agents that take irreversible actions whose effects interact, correctness requires that interacting decisions be evaluated against a coherent representation of reality at the moment they are made. We show that no existing system class satisfies this requirement and prove through the Composition Impossibility Theorem that independently advancing systems cannot be composed to provide Decision Coherence while preserving their native system classes. From this impossibility result, we derive Context Lake as a necessary system class with three requirements: (1) semantic operations as native capabilities, (2) transactional consistency over all decision-relevant state, and (3) operational envelopes bounding staleness and degradation under load. We formalize the architectural invariants, enforcement boundaries, and admissibility conditions required for correctness in collective agent systems. This position paper establishes the theoretical foundation for Context Lakes, identifies why existing architectures fail, and specifies what systems must guarantee for AI agents to operate constructively at scale.

翻译：人工智能代理正日益成为数据的主要消费者，它们持续运行以做出并发的、不可逆的决策。为人类分析周期设计的传统数据系统在这种运行机制下成为正确性瓶颈。当多个代理在共享资源上操作时，它们的行动在能够协调之前就已发生交互。因此，适用于决策窗口之后的正确性保证无法防止冲突。我们提出决策一致性定律：对于采取效果相互作用的不可逆行动的代理，正确性要求相互作用的决策在做出时必须基于一个连贯的现实表征进行评估。我们证明现有系统类别均无法满足此要求，并通过组合不可能性定理证明：独立推进的系统无法在保持其原生系统类别的同时通过组合来提供决策一致性。基于这一不可能性结果，我们推导出情境湖作为一个必要的系统类别，其具备三项要求：(1) 语义操作作为原生能力，(2) 对所有决策相关状态的事务一致性保证，以及(3) 界定负载下陈旧度与性能衰减的操作边界。我们形式化了集体代理系统中确保正确性所需的架构不变量、执行边界与可接纳条件。本立场论文建立了情境湖的理论基础，阐明了现有架构失效的原因，并明确了系统必须提供哪些保证才能使人工智能代理在大规模运行时具有建设性。